SOTAVerified

Stochastic Optimization

Stochastic Optimization is the task of optimizing certain objective functional by generating and using stochastic random variables. Usually the Stochastic Optimization is an iterative process of generating random variables that progressively finds out the minima or the maxima of the objective functional. Stochastic Optimization is usually applied in the non-convex functional spaces where the usual deterministic optimization such as linear or quadratic programming or their variants cannot be used.

Source: ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for Continuous Variables

Papers

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Showing 251–300 of 1387 papers

TitleStatusHype
Online Learning Quantum States with the Logarithmic Loss via VB-FTRL—0
Safe Collective Control under Noisy Inputs and Competing Constraints via Non-Smooth Barrier Functions—0
Riemannian stochastic optimization methods avoid strict saddle points—0
A Coreset-based, Tempered Variational Posterior for Accurate and Scalable Stochastic Gaussian Process Inference—0
Dealing with Structure Constraints in Evolutionary Pareto Set Learning—0
AdaSub: Stochastic Optimization Using Second-Order Information in Low-Dimensional SubspacesCode0
The Acquisition of Physical Knowledge in Generative Neural NetworksCode1
ROM-Based Stochastic Optimization for a Continuous Manufacturing Process—0
Sensor Attacks and Resilient Defense on HVAC Systems for Energy Market Signal Tracking—0
Nonlinear Filtering with Brenier Optimal Transport Maps—0
Differentially Private Distributed Stochastic Optimization with Time-Varying Sample Sizes—0
Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and Function—0
Resampling Stochastic Gradient Descent Cheaply for Efficient Uncertainty Quantification—0
Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout—0
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization—0
Differentially Private Non-convex Learning for Multi-layer Neural Networks—0
Why Do We Need Weight Decay in Modern Deep Learning?Code1
Smoothing Methods for Automatic Differentiation Across Conditional BranchesCode2
Federated Conditional Stochastic Optimization—0
Robust Nonparametric Hypothesis Testing to Understand Variability in Training Neural Networks—0
Robust Stochastic Optimization via Gradient Quantile Clipping—0
Markov Chain Mirror Descent On Data Federation—0
Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms—0
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian ProcessesCode0
Quasi-Monte Carlo for 3D Sliced WassersteinCode0
Distributionally Time-Varying Online Stochastic Optimization under Polyak-Łojasiewicz Condition with Application in Conditional Value-at-Risk Statistical Learning—0
A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty—0
Landscape-Sketch-Step: An AI/ML-Based Metaheuristic for Surrogate Optimization ProblemsCode0
Stochastic Learning of Semiparametric Monotone Index Models with Large Sample Size—0
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-ScaleCode0
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature EstimatesCode0
Backward error analysis and the qualitative behaviour of stochastic optimization algorithms: Application to stochastic coordinate descent—0
Likelihood-based inference and forecasting for trawl processes: a stochastic optimization approachCode0
A Unified Analysis on the Subgradient Upper Bounds for the Subgradient Methods Minimizing Composite Nonconvex, Nonsmooth and Non-Lipschitz Functions—0
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-DiversityCode0
A Homogenization Approach for Gradient-Dominated Stochastic Optimization—0
Adaptive pruning-based Newton's method for distributed learning—0
Stochastic Optimization of Coupled Power Distribution-Urban Transportation Network Operations with Autonomous Mobility on Demand Systems—0
Faster Stochastic Variance Reduction Methods for Compositional MiniMax Optimization—0
Path convergence of Markov chains on large graphs—0
Byzantine-Robust Decentralized Stochastic Optimization with Stochastic Gradient Noise-Independent Learning Error—0
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data MiningCode0
Outlier-robust neural network training: variation regularization meets trimmed loss to prevent functional breakdownCode0
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization—0
Proactive Resource Request for Disaster Response: A Deep Learning-based Optimization Model—0
The Marginal Value of Momentum for Small Learning Rate SGD—0
Stochastic Flight Plan Optimization—0
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization—0
High Probability Analysis for Non-Convex Stochastic Optimization with Clipping—0
Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case—0
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All datasetsCIFAR-100 WRN-28-10 - 200 EpochsCIFAR-10 WRN-28-10 - 200 EpochsCIFAR-10 ResNet-18 - 200 EpochsImageNet ResNet-50 - 90 EpochsPenn Treebank (Character Level) 3x1000 LSTM - 500 EpochsCIFAR-10CIFAR-100ImageNet ResNet-50 - 50 EpochsImageNet ResNet-50 - 60 EpochsAG NewsCoLAMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AvaGradAccuracy81.24—Unverified
2AdaShiftAccuracy81.12—Unverified
3Adam (eps-adjusted)Accuracy81.04—Unverified
4SGDAccuracy80.95—Unverified
5AdamWAccuracy79.87—Unverified
6AdaBoundAccuracy77.24—Unverified
#ModelMetricClaimedVerifiedStatus
1Adam (eps-adjusted)Accuracy96.36—Unverified
2AvaGradAccuracy96.2—Unverified
3SGDAccuracy96.14—Unverified
4AdaShiftAccuracy95.92—Unverified
5AdamWAccuracy95.89—Unverified
6AdaBoundAccuracy94.6—Unverified
#ModelMetricClaimedVerifiedStatus
1SGD - cosine LR scheduleAccuracy95.55—Unverified
2LookaheadAccuracy95.27—Unverified
3SGDAccuracy95.23—Unverified
4ADAMAccuracy94.84—Unverified
#ModelMetricClaimedVerifiedStatus
1AvaGradTop 1 Accuracy76.51—Unverified
2SGDTop 1 Accuracy75.99—Unverified
3AdamWTop 1 Accuracy72.9—Unverified
4AdaBoundTop 1 Accuracy72.01—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBoundBit per Character (BPC)2.86—Unverified
2AdaShiftBit per Character (BPC)1.27—Unverified
3AdamWBit per Character (BPC)1.23—Unverified
4AvaGradBit per Character (BPC)1.18—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet18Accuracy (max)86.85—Unverified
2Resnet34Accuracy (max)86.14—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet18Accuracy (max)58.48—Unverified
2Resnet34Accuracy (max)54.5—Unverified
#ModelMetricClaimedVerifiedStatus
1SGDTop 5 Accuracy92.15—Unverified
2LookaheadTop 1 Accuracy75.13—Unverified
#ModelMetricClaimedVerifiedStatus
1LookaheadTop 1 Accuracy75.49—Unverified
2SGDTop 1 Accuracy75.15—Unverified
#ModelMetricClaimedVerifiedStatus
1BertAccuracy (max)93.99—Unverified
#ModelMetricClaimedVerifiedStatus
1BertAccuracy (max)86.34—Unverified
#ModelMetricClaimedVerifiedStatus
1MLPNLL0.05—Unverified